Mammography mass detection: a multi-stage hybrid approach

Nima Sahba; Vahid Tavakoli; Alireza Ahmadian; Masoumeh Giti

doi:10.1117/12.810727

27 March 2009 Mammography mass detection: a multi-stage hybrid approach

Nima Sahba, Vahid Tavakoli, Alireza Ahmadian, Masoumeh Giti

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725947 (2009) https://doi.org/10.1117/12.810727
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Here in this paper a combined method of pixel based and region based mass detection is proposed. In the first step, the background and pectoral muscle are filtered from mammography images and the image contrast is enhanced using an adaptive density weighted approach. Then, in a coarse level, suspected regions are extracted based on mathematical morphology and adaptive thresholding methods. Finally, to reduce the false positives produced in the coarse stage, a useful feature vector based on ranklet transform is obtained and fed into a support vector machine classifier to detect masses. MIAS (Mammographic Image Analysis Society) and Imam Hospital databases were used to evaluate the performance of the algorithm. The sensitivity and specificity of the proposed method are 74% and 91% respectively. The proposed algorithm shows a high degree of robustness in detecting masses of different shapes.

Citation Download Citation

Nima Sahba, Vahid Tavakoli, Alireza Ahmadian, and Masoumeh Giti "Mammography mass detection: a multi-stage hybrid approach", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725947 (27 March 2009); https://doi.org/10.1117/12.810727

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KEYWORDS

Mammography

Feature extraction

Detection and tracking algorithms

Digital filtering

Databases

Lithium

Binary data

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